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This report describes the structure and content of an open dataset created for the purpose of correlating sky imagery, typical weather measurements, and hemispherical irradiance in an arc. The dataset includes the data for Albuquerque, NM (representing the desert southwest) and Eugene, OR (representing the pacific northwest).
The development of new ideas is the essence of scientific research. This is frequently done by developing models of physical processes and comparing model predictions with results from experiments. With models becoming ever more complex and data acquisition systems becoming more powerful, the researcher is burdened with wading through data ranging in volume up to a level of many terabytes and beyond. These data often come from multiple, heterogeneous sources and usually the methods for searching through it are at or near the manual level. In addition, current documentation methods are generally limited to researchers pen-and-paper style notebooks. Researchers may want to form constraint-based queries on a body of existing knowledge that is, itself, distributed over many different machines and environments and from the results of such queries then spawn additional queries, simulations, and data analyses in order to discover new insights into the problem being investigated. Currently, researchers are restricted to working within the boundaries of tools that are inefficient at probing current and legacy data to extend the knowledge of the problem at hand and reveal innovative and efficient solutions. A framework called the Project Integration Architecture is discussed that can address these desired functionalities.
Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. In this work, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI datasets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus facilitating applications of MSI in glycobiology.
Modifications made to the SBUV/2 (solar backscatter ultraviolet mod 2) to make it compatible with the Shuttle are examined. The Shuttle SBUV (SSBUV) in-flight radiometric calibration checking system is also described. Requirements, constraints, and methodology for combining SSBUV data with SBUV/2 data to remove its long-term drift are discussed.
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For a number of years there has been considerable interest in the earth's radiation budget (ERB) or energy balance, and entails making the best measurements possible of absorbed solar radiation, reflected shortwave radiation (RSW), thermal outgoing longwave radiation (OLR), and net radiation. ERB data are fundamental to the development of realistic climate models and studying natural and anthropogenic perturbations of the climate. Much of the interest and investigations in the earth's energy balance predated the age of earth-orbiting satellites (Hunt et al., 1986). Beginning in the mid 1960's earth-orbiting satellites began to play an important role in making measurements of the earth's radiation flux although much effort had gone into measuring ERB parameters prior to 1960 (House et al., 1986). Beginning in 1974 and extending until the present time, three different satellite experiments (not all operating at the same time) have been making radiation budget measurements almost continually in time. Two of the experiments were totally dedicated to making radiation budget measurements of the earth, and the other experiment flown on NOAA sun-synchronous AVHRR weather satellites produced radiation budget parameters as a by-product. The heat budget data from the AVHRR satellites began collecting data in June 1974 and have operated almost continuously for 23 years producing valuable data for long term climate monitoring.
State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
A Jan. - Jun. 1992 10 m height wind vector field was computed with the CMOD2 model function. Freilich and Dunbar used ESA measurements of AMI (Active Microwave Instrument) sigma-naught to derive a refined model function and to produce an alternate Jan. - Jun. 1992 10 m height wind vector field, named FD data. The ESA and FD model functions and algorithms were substantially different. The FD wind vectors were in better agreement with the climatological mean wind field and with moored buoy wind measurements. The average correlation coefficients between monthly mean and daily averaged FD and moored buoy matchups of the Cartesian wind components were 0.84 and 0.74, respectively. The average root mean square differences between the monthly mean and daily averaged FD and moored buoy matchups of the Cartesian wind components were 1.25 and 3.0 m/s, respectively.
A primary goal of the NASA Tropospheric Chemistry Program (TCP) is to "contribute substantially to scientific understanding of human impacts on the global troposphere". In order to analyze global or regional trends and factors of the troposphere chemistry, for example, its oxidation capacity or composition, a continuous global/regional data coverage as well as model simulations are needed. The Global Tropospheric Experiment (GTE), a major component of the TCP, provides data vital to these questions via aircraft measurement of key trace chemical species in various remote regions of the world. Another component in NASA's effort are satellite projects for exploration of tropospheric chemistry and dynamics. A unique data product is the Tropospheric Ozone Residual (TOR) utilizing global tropospheric ozone data. Another key research tool are simulation studies of atmospheric chemistry and dynamics for the theoretical understanding of the atmosphere, the extrapolation of observed trends, and for sensitivity studies assessing a changing anthropogenic impact to air chemistry and climate. In the context with model simulations, field data derived from satellites or (airborne) field missions are needed for two purposes: 1. To initialize and validate model simulations, and 2., to interpret field data by comparison to model simulation results in order to analyze global or regional trends and deviations from standard tropospheric chemistry and transport conditions as defined by the simulations. Currently, there is neither a sufficient global data coverage available nor are existing well established global circulation models. The NASA LARC CTM model is currently not yet in a state to accomplish a sufficient tropospheric chemistry simulation, so that the current research under this cooperative agreement focuses on utilizing field data products for direct interpretation. They will be also available for model testing and a later interpretation with a finally utilized model.
Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% 235 U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very few data exist for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there are other data with potential value available for developing into quality benchmarks for use in data- and software-validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers.
Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.
We used RNA-seq to compare gene expression from Zymomonas mobilis ZM4 grown under various conditions: aerobic ± paraquat, anaerobic ± hydrogen peroxide, or an iron chelator. We analyzed two mutant strains lacking predicted transcription factors (ZMO_0442 and ZMO_1411) grown under aerobic or anaerobic conditions. We report the RNA-seq data from these experiments.
The Global Learning and Observations to Benefit the Environment (GLOBE) citizen science program has recently conducted a series of month-long intensive observation periods (IOPs), asking the public to submit daily reports on cloud and sky conditions from all regions of Earth. This provides a wealth of crowdsourced observations from the ground, which complements other conventional scientific cloud data. In addition, the GLOBE reports are matched in space and time with geostationary and low Earth orbit satellites, which allows for a straightforward comparison of cloud properties, and minimizes the biases associated with mismatched sampling between participants and satellites. The matched GLOBE dataset is used to calculate the mean observed cloud cover by atmospheric level both worldwide and by region. The overall magnitudes of cloud cover between the GLOBE participants and the matched satellites agree within 10%, which is notable given the distinctly different natures of the data sources. The mean vertical cloud profiles show GLOBE reporting more low-level clouds and fewer high-level clouds than satellites. The low cloud disagreement is likely related to satellites missing low clouds when high clouds block their view. Conversely, the high cloud disagreement is related primarily to cloud opacity, as satellites may miss some optically thin clouds. Monte Carlo testing shows the results to be robust, and the tripled amount of IOP data reduces uncertainty by half. These findings also highlight ways in which citizen science IOP data may be used to support scientific research while accounting for their unique properties. Plain Language Summary: Citizen science is becoming an increasingly prominent aspect of scientific research, and so it important to study how citizen science data can be used effectively. For example, The GLOBE Program has recently conducted a series of special data-collecting events, or “challenges”, which gathered large numbers of reports on cloud and sky conditions. Because NASA GLOBE Clouds matches the participant reports with cloud observations from satellites, we can use these data to get a combined view of clouds from above and below. When looking at the average cloud cover for different atmospheric levels across Earth, we find that the GLOBE participants and the satellites agree quite closely. This is a surprising and fascinating find, given how different in nature volunteer ground reports are to satellite measurements. However, there are some small but notable disagreements between GLOBE participants and satellites about the distribution of cloud cover at different levels. In addition, by testing the data for uncertainty, we show that the results from the GLOBE data are reliable, and that more public participation improves the reliability. So, by carefully designing the analysis methodology, and by testing for the uncertainty of the data, citizen science can make a meaningful contribution to scientific research.
Total-ozone and ozone vertical profile results for Solar Backscattered Ultraviolet/Total Ozone Mapping Spectrometer (SBUV/TOMS) Nimbus 7 operation from November 1978 to November 1979 are available. The algorithm used have been thoroughly tested, the instrument performance has been examined in details, and the ozone results have been compared with Dobson, Umkehr, balloon, and rocket observations. The accuracy and precision of the satellite ozone data are good to at least within the ability of the ground truth to check and are self-consistent to within the specifications of the instrument. The 'SBUV User's Guide' describes the SBUV experiment and algorithms used. Detailed information on the data available on computer tape is provided including how to order tapes from the National Space Science Data Center.
The transcriptomes of Zymomonas mobilis 2032 were captured during the fermentation of ammonia fiber expansion (AFEX)-pretreated corn stover and switchgrass hydrolysates containing different concentrations of glucose and xylose. RNA samples were collected when Z. mobilis was fermenting glucose or xylose. Here, we present transcriptome sequencing (RNA-Seq) data obtained during separate phases of glucose or xylose consumption.
The mass of the W boson, a mediator of the weak force between elementary particles, is tightly constrained by the symmetries of the standard model of particle physics. After the observation of the Higgs boson, the last missing component of the model, the measurement of the W boson mass provides a stringent test of the model. A measurement of the W boson mass is presented, which used data corresponding to 8.8 fb$^{-1}$ of integrated luminosity, collected in proton-antiproton collisions at a 1.96 TeV center-of-mass energy with the CDF II detector at the Fermilab Tevatron collider. A sample of approximately 4 million W boson candidates was used to obtain a Wboson mass of 80433.5±6.4$_{stat}$ ±6.9$_{syst}$ = 80433.5±9.4 MeV, the precision of which exceeds that of all previous measurements combined. This measurement is in significant tension with the standard model expectation.
New seawater dielectric measurements have recently been made over a broad range of salinities and temperatures at the George Washington University (GW). These measurements have been used to refine the existing dielectric model function of seawater for salinity retrieval. The mathematical structure of the new model function has been investigated to: (a) look for a physical basis for the mathematical form; and (b) to optimize the accuracy of data fitting. The Debye model has been chosen to represent the dielectric constant of seawater as a function of salinity (S), temperature (T) and frequency (f). The retrieved salinity using the Debye model has good agreement with the in-situ data collected by Argo floats. The global differences in retrieved salinity and in-situ data will be presented at the meeting.
The James Webb Space Telescope (JWST) will provide multispectral and hyperspectral infrared images of a large number of astrophysical scenes. Multispectral images will have the highest angular resolution, while hyperspectral images (e.g., with integral field unit spectrometers) will provide the best spectral resolution. This paper aims at providing a comprehensive framework to generate an astrophysical scene and to simulate realistic hyperspectral and multispectral data acquired by two JWST instruments, namely, NIRCam Imager and NIRSpec IFU. We want to show that this simulation framework can be resorted to assess the benefits of fusing these images to recover an image of high spatial and spectral resolutions. To do so, we make a synthetic scene associated with a canonical infrared source, the Orion Bar. We develop forward models including corresponding noises for the two JWST instruments based on their physical features. JWST observations are then simulated by applying the forward models to the aforementioned synthetic scene. We test a dedicated fusion algorithm we developed on these simulated observations. We show that the fusion process reconstructs the high spatio-spectral resolution scene with a good accuracy on most areas, and we identify some limitations of the method to be tackled in future works. The synthetic scene and observations presented in the paper can be used, for instance, to evaluate instrument models, pipelines, or more sophisticated algorithms dedicated to JWST data analysis. Besides, fusion methods such as the one presented in this paper are shown to be promising tools to fully exploit the unprecedented capabilities of the JWST.